System for automatic diagnostics and monitoring of semiconductor defect die screening performance through overlay of defect and electrical test data

ABSTRACT

Systems and methods for determining a diagnosis of a screening system are disclosed. Such systems and methods include identifying defect results based on inline characterization tool data, identifying electrical test results based on electrical test data, generating one or more correlation metrics based on the defect results and the electrical test results, and determining at least one diagnosis of the screening system based on the one or more correlation metrics, the diagnosis corresponding to a performance of the screening system.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit under 35 U.S.C. § 119(e) ofU.S. Provisional Application Ser. No. 63/303,977 filed on Jan. 27, 2022,titled “System for automatic diagnostics and monitoring of semiconductordefect die screening performance through overlay of defect andelectrical test data”, which is incorporated herein by reference in theentirety.

TECHNICAL FIELD

The present disclosure relates generally to die screening systems, and,more particularly, to diagnostics of performance of die screeningsystems.

BACKGROUND

In the course of manufacturing a semiconductor device, a wafer goesthrough hundreds of processing steps to pattern a functioning device.Over the course of these steps, inspection and metrology steps areperformed to ensure the process is in control and will produce afunctioning product at the end of the manufacturing cycle. Inspectiontools may find unintended defects in the patterned structures of thedevices, while metrology tools may measure the physical parameters offeatures of the device (e.g., film thickness, patterns, overlay, etc.)versus the intended physical parameters. Electrical test tools (e.g.,electric probes) may also be used to test for defects by testing forproper electrical function of a device.

Risk averse users of semiconductor devices, such as automotive,military, aeronautical, and medical applications, need failure rates inthe Parts per Billion (PPB) range, well below typical rates. Recognizingand screening out devices that do fail or may fail in the future is keyto meeting these industry requirements. While some defects and metrologyerrors may be so significant as to clearly indicate a device failure,lesser variations may have an unclear effect. A portion of these lesserdefects (e.g., latent reliability defects) may later go on to causeearly reliability failures of the device after exposure to its workingenvironment. A variety of factors may affect the ability to accuratelyscreen for devices that fail or may fail in the future. For example, itis not always possible to quickly know when a component used in ascreening process is accurately calibrated or functioning properly.

SUMMARY

A screening system is disclosed, in accordance with one or moreembodiments of the present disclosure. In one illustrative embodiment,the screening system includes a controller communicatively coupled toone or more sample analysis tools. In another illustrative embodiment,the controller includes one or more processors and memory. In anotherillustrative embodiment, the memory is configured to store a set ofprogram instructions. In another illustrative embodiment, the one ormore processors are configured to execute program instructions causingthe one or more processors to identify defect results for a populationof dies based on inline characterization tool data received from the atleast one inline characterization tool of the one or more sampleanalysis tools. In another illustrative embodiment, the one or moreprocessors are configured to execute program instructions causing theone or more processors to identify electrical test results for thepopulation of dies based on electrical test data received from the atleast one electrical test tool of the one or more sample analysis tools.In another illustrative embodiment, the one or more processors areconfigured to execute program instructions causing the one or moreprocessors to generate one or more correlation metrics based on theidentified defect results and the identified electrical test results. Inanother illustrative embodiment, the one or more processors areconfigured to execute program instructions causing the one or moreprocessors to determine at least one diagnosis of the screening systembased on the one or more correlation metrics, the at least one diagnosiscorresponding to a performance of the screening system.

A method for screening is disclosed, in accordance with one or moreembodiments of the present disclosure. In one illustrative embodiment,the method includes identifying defect results for a population of diesbased on inline characterization tool data received from at least oneinline characterization tool of one or more sample analysis tools of ascreening system. In one illustrative embodiment, the method includesidentifying electrical test results for the population of dies based onelectrical test data received from at least one electrical test tool ofthe one or more sample analysis tools. In one illustrative embodiment,the method includes generating one or more correlation metrics based onthe identified defect results and the identified electrical testresults. In one illustrative embodiment, the method includes determiningat least one diagnosis of the screening system based on the one or morecorrelation metrics, the at least one diagnosis corresponding to aperformance of the screening system.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not necessarily restrictive of the invention as claimed. Theaccompanying drawings, which are incorporated in and constitute a partof the specification, illustrative embodiments of the invention andtogether with the general description, serve to explain the principlesof the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The numerous advantages of the disclosure may be better understood bythose skilled in the art by reference to the accompanying figures inwhich:

FIG. 1A illustrates a schematic block diagram of a screening system, inaccordance with one or more embodiments of the present disclosure;

FIG. 1B illustrates a block diagram of a screening system, in accordancewith one or more embodiments of the present disclosure; and

FIG. 2 illustrates a flow diagram depicting a method or process fordetermining a diagnosis of a screening system, in accordance with one ormore embodiments of the present disclosure.

FIG. 3A illustrates a diagrammatic three-dimensional representation of adefect, in accordance with one or more embodiments of the presentdisclosure;

FIG. 3B illustrates a diagrammatic three-dimensional representation of alatent reliability defect, in accordance with one or more embodiments ofthe present disclosure;

FIG. 4A illustrates a graphical representation of an accuracy of adefect classifier over time, in accordance with one or more embodimentsof the present disclosure;

FIG. 4B illustrates a graphical representation of false positive ratesof a defect classifier, in accordance with one or more embodiments ofthe present disclosure.

FIG. 5 illustrates a graphical representation of sorted defect resultsthat are color coded to match corresponding electrical test results, inaccordance with one or more embodiments of the present disclosure;

FIG. 6A illustrates a graphical representation of a process controlchart, in accordance with one or more embodiments of the presentdisclosure;

FIG. 6B illustrates a graphical representation of a process controlchart of a die-misalignment associated with a screening of a wafer, inaccordance with one or more embodiments of the present disclosure.

FIG. 6C illustrates a diagrammatic representation of the wafer of FIG.6B, in accordance with one or more embodiments of the presentdisclosure;

FIG. 7 illustrates a graphical representation of test coverage and testtime usable in an electrical test program assessment, in accordance withone or more embodiments of the present disclosure; and

FIG. 8 illustrates a flow diagram depicting a method or process fordetermining a diagnosis of a screening system, in accordance with one ormore embodiments of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the subject matter disclosed,which is illustrated in the accompanying drawings. The presentdisclosure has been particularly shown and described with respect tocertain embodiments and specific features thereof. The embodiments setforth herein are taken to be illustrative rather than limiting. Itshould be readily apparent to those of ordinary skill in the art thatvarious changes and modifications in form and detail may be made withoutdeparting from the spirit and scope of the disclosure.

Embodiments of the present disclosure are directed to determining adiagnosis of a performance of a screening system. For example,embodiments of the present disclosure are directed to determining adiagnosis of a performance using correlation metrics, which may becalculated based on a comparison of defect results and electrical testresults.

Generally, wafer dies with non-latent defects 304 and latent defects 306on features 302 of a device (that cause a failure or are likely to causea failure in the future; see FIGS. 3A and 3B) may be screened out (e.g.,out of the supply chain) using one or more of a variety of techniques ofa screening system 100, such as inline characterization methods (e.g.,inline defect inspection methods) or test methods (e.g., electrical testmethods). In a general sense, inline characterization methods thatgenerate defect results may be optical-based, but test methods thatgenerate test results are not generally optical based (e.g., but notlimited to, electric test probes). However, such techniques mayunknowingly lose accuracy. For instance, a defect classifier of aninline characterization method may degrade in performance over time. Achange in the number of detected defects could be caused by an actualchange in the number of defects or the change may, for example, be a‘false negative’ caused by an inaccurate screening system thatunder-detects defects.

Typically, a human may need to periodically check the accuracy of defectresults of a defect classifier (e.g., manually check a sample subsetusing a scanning electron microscope (SEM)). Such a periodic check canbe labor intensive, slow, and costly. It is noted that this example isfor illustrative purposes only, and there are many other possible causesof a loss of performance of a screening system. Without such a periodiccheck, a screening process may operate for long periods of time before alack of performance is detected. Therein lies a need for providingmethods and systems for diagnosing a performance of a screening system.

It is contemplated that if it is known when (and/or why) the screeningsystem performance changed then a multitude of benefits may result.These benefits include, but are not limited to, reducing the labor/costneeded to check for an inaccuracy (e.g., reducing/eliminating thefrequency of periodic manual checks); reducing the number of dies/wafersthat need to be re-screened when an inaccuracy is detected (e.g., byquickly determining the point in time that an inaccuracy began); andreducing the time/labor to diagnose the cause of an inaccuracy (e.g., ifthe cause can be automatically determined/narrowed). Further benefitsmay include increased situational awareness of the screening systemperformance generally, more accurate defect detection (e.g., to achieveParts per Billion reliability), and higher yield.

It is contemplated herein that a correlation may exist, at least in somedies (e.g., wafer lots) and screening systems/methods, between defectresults using inline defect detection methods and test results usingtest methods. For example, in one sense a correlation can be analogizedbroadly as the more defects (e.g., latent defects) detected on aparticular die (e.g., indicative of a higher chance of failure), themore likely that that particular die is to fail a test (e.g., electricalprobe test).

Further, it is contemplated, that such a correlation (or a lack thereof)may be reliable enough to be used to determine a performance/accuracy ofthe screening system. However, it should be noted that such acorrelation may not historically or necessarily be reliable enough. Forexample, defect results have historically been extremely noisy and itmay only be through recent advancements (e.g., I-PAT defect classifiers)that such results have a low enough noise level to be reliably used forsuch a correlation in the context of determining performance inembodiments of the present disclosure.

The correlation may be embodied in one or more different correlationmetrics (e.g., but not limited to, ratios of results, ratios of binneddies based on the results, and any other metric of the results). Furtherstill, in some embodiments, the correlation (e.g., one or morecorrelation metrics) may be used to determine not just when aperformance of the screening system has changed, but what the cause ofthe change is (e.g., to one or more degrees of specificity and/orlikelihood), and/or what improvement should be made to correct for thechange/inaccuracy.

As used herein, the term “diagnosis”, diagnostics, and the like may meandetermining the performance (e.g., quantifiable accuracy, lack ofaccuracy, change in performance, and the like) and/or determining asource of a change in performance (e.g., whether the likely source isthe inline characterization tool or the electrical test tool). It isnoted herein that in some embodiments, as disclosed in figures anddescriptions herein and based on experimental data (e.g.,proof-of-concept studies of over 100,000 dies), the reliability of suchdeterminations are sufficient for use in a screening system/method tocause at least some of the benefits described above.

FIGS. 1A-8 generally illustrate a system and method for determining adiagnosis of a screening system, in accordance with one or moreembodiments of the present disclosure. In at least some embodiments, thesystem and method may be used to augment existing methods formaintenance of a screening system.

Referring now to FIG. 1A, a schematic block diagram of a screeningsystem 100 is illustrated, in accordance with one or more embodiments ofthe present disclosure.

The screening system 100 may include, but is not limited to, one or moresample analysis tools (e.g., characterization tools 112, test tools114). The characterization tools 112 may include, but are not limitedto, an inspection tool 140 and/or a metrology tool 142. The test tools114 may include an electrical test tool 144 and/or a stress test tool146. The screening system 100 may additionally include, but is notlimited to, a controller 122 including one or more processors 124, amemory 126, and a user interface 102. The screening system 100 may beconfigured to screen a population of samples 104, but does notnecessarily comprise the population of samples 104 (e.g., dies). Forexample, the population of samples 104 may be at least one of dies in asample, dies in multiple samples in a lot, or dies in multiple samplesin multiple lots. The characterization tools may be configured to beused to generate defect results 116 and the test tools 114 may beconfigured to be used to generate test results 118.

In embodiments, characterization tools 112 may be any tool used in theart for sample 104 characterization, such as an inspection tool 140and/or metrology tool 142. Results generated from a characterizationtool 112 may be defect results 116 (e.g., based on inlinecharacterization tool data received by (generated using) thecharacterization tool 112) and may be stored in memory 126.

In one non-limiting example, the characterization tools 112 may includean inspection tool 140 (e.g., an inline sample analysis tool) fordetecting defects in one or more layers of a sample 104. The screeningsystem 100 may generally include any type of inspection tools 140. Forexample, an inspection tool 140 may include an optical inspection toolconfigured to detect defects based on interrogation of the sample 104with light from any source such as, but not limited to, a laser source,a lamp source, an X-ray source, or a broadband plasma source. By way ofanother example, an inspection tool 140 may include a particle-beaminspection tool configured to detect defects based on interrogation ofthe sample with one or more particle beams such as, but not limited to,an electron beam, an ion beam, or a neutral particle beam. For instance,the inspection tool 140 may include a transmission electron microscope(TEM) or a scanning electron microscope (SEM). For purposes of thepresent disclosure, it is noted herein the at least one inspection tool140 may be a single inspection tool 140 or may represent a group ofinspection tools 140.

For the purposes of the present disclosure, the term “defect” may referto a physical defect found by an inline inspection tool, a metrologymeasurement outlier, or other physical characteristic of thesemiconductor device that is deemed to be an anomaly. A defect may beconsidered to be any deviation of a fabricated layer or a fabricatedpattern in a layer from design characteristics including, but notlimited to, physical, mechanical, chemical, or optical properties. Inaddition, a defect may be considered to be any deviation in alignment orjoining of components in a fabricated semiconductor die package.Further, a defect may have any size relative to a semiconductor die orfeatures thereon. In this way, a defect may be smaller than asemiconductor die (e.g., on the scale of one or more patterned features)or may be larger than a semiconductor die (e.g., as part of awafer-scale scratch or pattern). For example, a defect may includedeviation of a thickness or composition of a sample layer before orafter patterning. By way of another example, a defect may include adeviation of a size, shape, orientation, or position of a patternedfeature. By way of another example, a defect may include imperfectionsassociated with lithography and/or etching steps such as, but notlimited to, bridges between adjacent structures (or lack thereof), pits,or holes. By way of another example, a defect may include a damagedportion of a sample 104 such as, but not limited to, a scratch, or achip. For instance, a severity of the defect (e.g., the length of ascratch, the depth of a pit, measured magnitude or polarity of thedefect, or the like) may be of importance and taken into consideration.By way of another example, a defect may include a foreign particleintroduced to the sample 104. By way of another example, a defect may bea misaligned and/or mis-joined package component on the sample 104.Accordingly, it is to be understood that examples of defects in thepresent disclosure are provided solely for illustrative purposes andshould not be interpreted as limiting.

In another non-limiting example, the characterization tools 112 mayinclude a metrology tool 142 (e.g., an inline sample analysis tool) formeasuring one or more properties of the sample 104 or one or more layersthereof. For example, a metrology tool 142 may characterize propertiessuch as, but not limited to, layer thickness, layer composition,critical dimension (CD), overlay, or lithographic processing parameters(e.g., intensity or dose of illumination during a lithographic step). Inthis regard, a metrology tool 142 may provide information about thefabrication of the sample 104, one or more layers of the sample 104, orone or more semiconductor dies of the sample 104 that may be relevant tothe probability of manufacturing defects that may lead to reliabilityissues for the resulting fabricated devices. For purposes of the presentdisclosure, it is noted herein the at least one metrology tool 142 maybe a single metrology tool 142 or may represent a group of metrologytools 142.

In embodiments, a test tool 114 may include any number of test tools andtest tool types used in the art for tests of samples 104 and testresults 118. For example, a test tool 114 may include an electrical testtool 114 (e.g., configured to generate electrical burn-in test results,electrical probe test results, final electrical test results, afterfinal test results, and the like). Such results may be test results 118(e.g., electrical test results 118 based on electrical test datareceived by (generated using) the electrical test tool 144) and may bestored in memory 126.

Referring now to FIG. 1B, a block diagram of a screening system 100 isillustrated, in accordance with one or more embodiments of the presentdisclosure.

The screening system 100 may include characterization tools 112 (e.g.,inspection tool 140 and/or metrology tool 142 of FIG. 1A) and test tools114 (e.g., electrical test tool 144 of FIG. 1A) configured to screen apopulation of samples 104. The population of samples 104 may be screenedin any order (sequentially and/or in parallel) by any technique,individually, by the lot, once or multiple times, and the like. Forexample, the population of samples 104 may be initially screened inlineby a characterization tool 112 (e.g., inline characterization tool 112)(as shown by material flow 104 a) at multiple critical manufacturingsteps of the multiple layers of the population of samples 104. Next, forexample, at or near the end of the manufacturing process, the populationof samples 104 may be screened by the test tool 114 (e.g., to performone or more electrical tests) (as shown by material flow 104 b).

In some embodiments, defect results 116 are not limited to data (e.g.,sensor data) received from (and/or by) the characterization tool 112(e.g., from a sensor thereof), but may be derived from such data (e.g.,in a defect-based classifying step 220).

For example, a defect classifier 120 may be used to obtain such defectresults 116. The defect classifier 120 may be any defect classifier. Forexample, the defect classifier 120 may apply algorithms (machinelearning, heuristic code, or otherwise) to discern characteristics ofeach defect detected by an inspection tool 140 to classify the defectinto a category, discern multiple characteristics of the defect, and thelike. For instance, the defects may be classified as killer/reliability,defect of interest, and nuisance. Appropriate weighting may then beassigned to each defect in a die based on such a classification schemeto determine an overall weighted score for the die (e.g., I-PAT score).The die score may be used to determine whether a die should be screenedout (e.g., binned).

Production implementation of inline defect die screening may require atight control of a defect classifier 120 performance, inspection tool140 health, and/or inspection tool 140 recipe. If an unreliable die ismisclassified as meeting a reliability threshold, then a potentialreliability failure may enter the supply chain (i.e., under-kill,false-negative). Conversely, if a reliable die is misclassified asunreliable and removed from the supply chain then the fabricationprocess incurs unnecessary yield loss (i.e., overkill, false-positive).

Such misclassifications can stem from many sources, including but notlimited to: inherent limitations in defect classifier 120 performanceassociated with defect attribute resolution from the inspection tool140, availability of adequate training images during defect classifier120 setup, and/or defect classifier algorithm performance; drift overtime associated with changes in fabrication processing conditions,defect morphology, and/or new device implementation (e.g., variations infilm thickness, while still within the device control limits, canslightly alter a defect's appearance to the screening system 100);misalignment of defect-based die coordinates with the inking/test diecoordinates, which can lead to a catastrophic drop in defect classifierperformance; changes to inspection tool performance; changes toinspection tool recipe; and/or the like). As a result, operators mayneed to spend a significant amount of time monitoring and updatingdefect classifier performance to ensure minimum overkill and underkill.

The defect classifier 120 may be an element of (or separate from) thecharacterization tool 112 (e.g., on the same or different controller).The defect classifier 120 may generally, but is not limited to, beconfigured to provide a variety of “defect-based” results 116 based oncharacterization tool data. For example, an inspection tool 140 may beused to determine results corresponding to methods of, but not limitedto, detecting, re-detecting, characterizing, and/or categorizing defects(latent and/or non-latent). Further, the results of such methods maythemselves be further used (e.g., in a defect-based classifying step220; using the defect classifier 120; and the like) to performadditional analysis. For example, such results may be used to furtheranalyze one or more die/wafer reliability (e.g., binning dies asacceptable or discardable (and such binning may be defect results 116)).For instance, an I-PAT defect classifier 120 may be used todetect/characterize defects and determine binning attributes asdisclosed in U.S. Pat. No. 10,761,128, filed on Apr. 5, 2017, entitled“Methods and Systems for Inline Parts Average Testing and LatentReliability Defect Detection”, which is hereby incorporated by referencein its entirety. It is noted that the examples above are forillustrative purposes only, and any defect detection methods and systemsmay be used to achieve any defect-based results 116.

In some embodiments, even though an I-PAT defect classifier 120 maygenerally (e.g., in other disclosures) utilize a variety of types oftest tool 114 based data to detect/characterize defects, the defectresults 116 of the present disclosure may be based on data that does notinclude test tool data, such that the defect results 116 and the testresults 118 (e.g., electrical test results) are based on mutuallyexclusive sources of data such that each is an independent indication ofa reliability of the population of dies 104. Benefits of such arestriction at least include increasing a signal to noise ratio of theone or more correlation metrics 130. For example, defect results 116 maybe based on the detection of physical anomalies on the sample 104 duringthe device manufacturing process while the test results 118 may be basedon the electrical performance of the completed device.

In at least one embodiment, the screening system 100 includes acorrelation module 106. In some embodiments, the correlation module 106may be configured to generate one or more correlation metrics 130 basedon the identified defect results 116 and the identified test results118. Note that the identified defect results 116 may be in their rawform and/or in a processed form (e.g., after processed by a defectclassifier 120 such as I-PAT, be in a die-binned form representing whichdies are binned, or any other derived form). As noted above, acorrelation between defect results 116 and test results 118 may beembodied in one or more (different) correlation metrics 130. In someexamples, the correlation module 106 may be called a “Defect-to-TestCorrelation Engine”.

A module may mean, but is not limited to, program instructions (e.g.,heuristic code, a subset of program instructions, a separateapplication, on the same/different controller, and/or the like),dedicated hardware/circuitry (logic gate) layouts, and/or the like.

In at least one embodiment, the screening system 100 includes adiagnostic module 108 configured to determine (output, generate, and thelike) at least one diagnosis 132. In one example, the diagnostic module108 includes a machine learning module 134.

The machine learning module may be any machine learning moduleconfigured to correlate the correlation metrics 130 to diagnosis 132(and may be trained on similar multiple sets of training correlationmetrics and multiple sets of one or more training diagnosis). Note thatthe diagnostic module 108 may be any module (e.g.,conventional/heuristic program instructions) and does not necessarilyinclude a machine learning module 134 as shown. In some examples, thediagnostic module 108 may be called a “Diagnostics Wizard”. In at leastsome embodiments, the diagnostic module 108 may be configured todeconvolve the correlation metrics 130 to determine a root cause of aperformance change (e.g., classifier degradation, die grid misalignment,inspector system degradation, inspector recipe issues, testermalfunction, or some other cause). For example, the machine learningmodule 134 may be configured to automatically determine the root cause.

Referring now to FIG. 2 , a flow diagram 200 depicting a method orprocess for determining a diagnosis 132 of a screening system 100 isillustrated, in accordance with one or more embodiments of the presentdisclosure.

A defect detecting step 212 using characterization tools 112 is shownand may comprise multiple layer operations 204 at critical manufacturingsteps, and data therefrom may be aggregated (before and/or after adefect-based classifying step 220), in accordance with one or moreembodiments. In at least some embodiments, 100 percent of samples 104(e.g., 100 percent of dies) are screened at a defect detecting step 212.For example, samples 104 may be screened using an inline defectinspection tool 140 (e.g., and metrology data of a metrology tool 142).

For example, data 116 a from the defect detecting step 212 may be rawsensor data and/or at least partially processed/aggregated dataindicative of a number of defects detected, classified, characterized,and/or the like. Such raw and/or processed data 116 a may be equivalentto defect results 116 shown in FIG. 1B in the sense that the data 116 amay be ready to be used in a correlating step 206, or, alternatively,the data 116 a (at least a portion thereof) may be used (e.g.,aggregated, used in a module, and the like) in an optional defect-basedclassifying step 220 to generate defect results 116 b. For example, adefect-based classifying step 220 may be used to generate defect results116 b via a defect classifier 120 such as an I-PAT defect classifierbased on characterization data 116 a (e.g., inline characterization tooldata) of one or more characterization tools 112. Such defect results 116b may be defect results 116.

In another example, defect results 116 b may be based on statisticaloutlier analysis such as G-PAT, P-PAT, Z-PAT, and the like.

In at least one embodiment, defects may be identified using anycombination of characterization tools 112 (e.g., inspection tools 140,metrology tools 142 for use in a defect classifier, or the like), whichare utilized before or after one or more layer operations 204 (e.g.,lithography, etching, aligning, joining, or the like) for layers ofinterest in the semiconductor dies and/or semiconductor die packages. Inthis regard, the defect detecting step 212 at various stages of themanufacturing process may be referred to as inline defect detection.Note that the metrology tools 142 may not necessarily be used todirectly image defects, but data therefrom (e.g., film thicknesses,etc.) may be used in a defect detecting step 212 (e.g., I-PATclassifier) to improve the accuracy of defectdetection/characterization.

A testing and test-based classifying step 214 using test tools 114 isshown, in accordance with one or more embodiments. The testing andtest-based classifying step 214 may use any test tool to analyze thereliability of a sample (e.g., die). For example, the testing andtest-based classifying step 214 may include binning dies based onelectrical test results 118 using an electrical test tool 144 (e.g.,and/or the electrical test results 118 may itself include/be binned diessuch that, for example, a ratio of binned dies may be calculated as acorrelation metric).

At least some embodiments include a correlating step 206. For example,the defect results 116 from the characterization tools 112 (e.g.,utilizing an I-PAT defect classifier 120) and the test results 118 maybe aggregated by a correlation module 106 (as shown in FIG. 1B) togenerate one or more correlation metrics 130.

At least some embodiments include a diagnosing step 208. For example,the one or more correlation metrics 130 may be used by a diagnosticmodule 108 (shown in FIG. 1B) to generate and/or determine at least onediagnosis 132 of a performance of the screening system 100.

The diagnosis 132 may be (e.g., or include) a degradation diagnosisindicative of a relatively low defect classifier performance (e.g.,lower than previously) of the defect classifier 120.

The diagnosis 132 may be a die-level misalignment diagnosis of a sampleanalysis tool, as is shown and described below in relation to FIGS. 6Band 6C. The die-level misalignment diagnosis may be indicative of a diemisalignment of the at least one test tool 114 relative to the at leastone inline characterization tool 112.

The diagnosis 132 may be a defect recipe deviation diagnosis indicativeof a change in an inline defect recipe of the at least one inlinecharacterization tool. For example, screening system recipes (e.g.,inspection recipes) may be changed without knowledge, causing anunrecorded change in performance of the screening system 100. The defectrecipe deviation diagnosis may be an inline inspection defect recipedeviation diagnosis. In one example, an unauthorized change to aproduction recipe adversely affects performance. In another example, aninline defect inspection recipe associated with the screening system 100is inadvertently changed to the recipe for baseline defect inspectionprocess control. The examples above are for illustrative purposes only,and many other examples may occur, such as in less sophisticated factoryautomation systems of 200 mm and 150 mm processes.

The diagnosis 132 may be an inline characterization tool deviationdiagnosis indicative of a deviation in at least one of hardware orsoftware of the at least one inline characterization tool. For example,the hardware may include inspection tool 140 hardware. For example, thehardware may include a degrading illumination source (e.g., which maycause a reduction in capture rate). While many systems may haveautomatic monitoring and calibration of the illumination source, such anunexpected failure mode may still occur without embodiments of thepresent disclosure. Further, an improvement of the performance of thescreening system may be determined, which may include replacing thedegrading illumination source.

The diagnosis 132 may be a predicted maintenance interval diagnosisindicative of a predicted maintenance interval of a component of thescreening system. Alternatively, the predicted maintenance intervaldiagnosis may be a predicted maintenance interval improvement.

The diagnosis 132 may be an electrical tool deviation diagnosisindicative of a deviation in a performance of the at least oneelectrical test tool 144. For example, a tip of an electrical probe tool144 may be damaged, degrade, and/or the like and produce inaccuratemeasurements without knowledge by a user of the screening system 100.

The diagnosis 132 may be a screening system method degradation diagnosisindicative of a deviation in a performance of a screening system methodas changes are made to the screening system method.

The one or more correlation metrics 130 may be generated automatically(e.g., on die populations large enough to provide a statisticallysignificant representation of performance). Examples of a statisticallysignificant representation of performance may include at least one of,but are not limited to, a lot of at least 5, 25, or the like wafers; arolling average of a certain number of lots (e.g., at least 5) (see FIG.6A); or the like.

The one or more correlation metrics 130 may include a binning ratiometric corresponding to a ratio between a number of dies of thepopulation of dies binned for removal based upon the identified defectresults 116 and a number of dies of the population of dies binned forremoval (e.g., ultimately binned for removal) based upon the identifiedtest results 118.

The one or more correlation metrics 130 may include a classifierconfidence metric corresponding to an aggregate confidence score (e.g.,defect score of FIG. 5 , such as an aggregate confidence score, I-PATscore, defect guided Z-PAT, and/or the like) of the defect classifier120.

The one or more correlation metrics 130 may include one or moreper-class correlation metrics corresponding to one or more correlationsbetween a class of defect results (e.g., aggregate confidence score perclass) and the test results 118. For example, defects results may befiltered by their class (e.g., nuisance defect, defect of interest, andthe like) and each class, or a particular subset of one or more classes,may be used to generate the one or more correlation metrics. Forexample, each correlation metric may be based on a different class ofdefect results. Classes may be determined using any method/tool known inthe art, including any characterization tool (e.g., inspection tool 140and defect classifier 120). For instance, some defects may have astronger correlation with post package test and post burn-in final testthan other defects. In some examples, a correlation metric is generatedfor each class of defect to each test result (of multiple types of testresults).

The one or more correlation metrics 130 may include one or moreper-class derivative correlation metrics corresponding to one or morederivative correlations between a derivative of an attribute of one ormore attributes of a class of defect results and the electrical testresults. For example, an attribute may include any attribute of a defect(e.g., size, type, shape, location, and the like, or any otherattribute) and be determined by any method/tool known in the art,including any characterization tool. For example, a derivative may meana generated correlation, filtering, computation, subclass, or any otherderivation based upon one or more attributes of a class. For instance,the derivative correlations may be first derivatives. For example, anormal curve profile of defect attributes could be tracked (e.g., topten most important attributes to a defect classifier) per class tomonitor significant deviations. Such deviations may indicate a recipechange or a tool related issue. Further, a limited time series may beused, which may allow for removal of recipe changes as an extraneousvariable. In addition, such a limited time series may allow forisolation of a recipe change by tracking changes from a time that thedefect classifier was implemented.

In an optional step (not shown), an improvement may be determined basedon the diagnosis 132. The improvement may be indicative of one or moresteps that may manually and/or automatically be performed to improve thescreening system 100.

In some embodiments, an improvement includes (is configured for)reducing at least one of a false positive rate or a false negative rateof the at least one inline characterization tool (and/or the electricaltest tool). For example, in some embodiments, the defect classifier 120performance is improved overall with fewer reliability escapes and/orless yield loss.

In one example, a performance of an inline defect classifier 120degrades over time for one or more reasons (e.g., different filmthickness, different types of defect characteristics not before trainedon, etc.) and an improvement (e.g., a defect classifier improvement) isdetermined to address this issue. For example, an alert may be sent to auser that the inline defect classifier 120 has degraded and should beretrained/recalibrated based on a diagnosis that the inline defectclassifier 120 is degraded. In this regard, a predictive maintenance ofthe defect classifier 120 may be determined.

In some embodiments, the improvement may be indicative of (correspondto) at least one of: 1) adjusting of at least one of an attribute orthreshold of the defect classifier; or 2) retraining of a machinelearning model of the defect classifier. For example, the improvementmay be, but is not limited to, a communication that is sent to a user toalert the user that at least one of the above steps should be taken. Theabove example is for illustrative purposes only, and the improvement maybe any improvement to any step or element of the screening system 100and be determined by any controller, module, and the like (e.g., anoutput of the machine learning module 134 of the diagnostic module 108).In this regard, the correlation metrics (and/or the defect results andelectrical test results) may be used to determine a root cause of aperformance change of the screening system 100 and indicate/communicatethat a corresponding improvement (fix) should be performed.

Referring now to FIG. 4A, a graphical representation 408 of accuracies(e.g., indicative of a performance) of a defect classifier 120 over timeis illustrated. As shown, a defect classifier accuracy withoutintervention 404 may naturally decrease over time due to a variety offactors (e.g., changes in film thickness of the samples 104, not beingtrained on the types of images obtained by the characterization tool112, and any other reason causing degradation). However, a defectclassifier accuracy with intervention 402 may maintain accuracy overtime (e.g., due to periodic manual checks, retraining, calibrating, andthe like; and/or due to interventions (actions based on improvementdeterminations) of embodiments of this disclosure).

In an optional step, not shown, adaptive sampling of defect classifier120 maintenance is determined based on correlation metrics 130.

In an optional step, not shown, a maintenance frequency of a defectclassifier of the defect classifier 120 is determined based oncorrelation metrics 130.

In an optional step, not shown, an emergence of a new class of defectsis determined based on correlation metrics 130.

In an optional step, not shown, a reduced required frequency of a manualspot-check defect classifier maintenance of the defect classifier 120 isdetermined based on correlation metrics 130. In this regard, a“predictive maintenance” benefit may be obtained. Traditional systemsfor monitoring defect classifier performance for inline defect diescreening may rely on a defect-by-defect comparison of the automaticdefect classifier 120 results (e.g., defect results 116 b) with manualclassification results from a human expert. This may be labor-intensiveand required to be performed periodically (e.g., weekly or monthly) on asmall subset of the defect population (e.g., <1%). Example results ofsuch a comparison may be characterized by many different metrics (e.g.,accuracy, purity, precision, recall, F1 score) and are charted as shownin FIG. 4B and Table 1 below.

Referring now to FIG. 4B, a graphical representation 400 (e.g., ReceiverOperating Characteristic (ROC) curve) of false positive rates and truepositive rates of a defect classifier 120 is illustrated. As shown, acompletely random prediction classifier model will generaterandom/useless results 410. Other models show various results 412, 414,416 that are better than random guessing. Results 412 show a high truepositive rate of an effective defect classifier model.

TABLE 1 Predicted Predicted Predicted Class 1 Class 2 Class 3 PurityClass 1: Nuisance 5539 196 127 94% Class 2: DOI 143 1731 94 88% Class 3:Killer 144 95 2922 92% Accuracy 95% 86% 93%

Table 1 above illustrates an example Confusion Matrix, which istypically used to compare defect classifier model-predicted results(columns) with results achieved manually by an expert (rows). The largenumbers 5539, 1731, and 2922 show areas of agreement between what theclassifier model predicted/identified is a Class 1, 2, or 3 defect andwhat the expert identified as a Class 1, 2, or 3 defect.

Generally, an ROC curve and/or a Confusion Matrix method may be methodsfor maintaining an (at least partially) unambiguous measure ofperformance of at least some sample analysis tools (e.g., in a processcontrol system). Further, performing such methods at the defect level(e.g., rather than the die level) may generally allow for actionableinsight into defect classifier improvements to make (e.g., which classesof defects are not being detected well). However, limitations of such amethod indicative of being resource intensive may include being relianton a human expert, being reliant on optical based tool and/or scanningelectron microscope, being time consuming, and using increasingresources as a function of the number of screening steps. Otherlimitations may include utilizing a limited number of samples,inconsistency (e.g., confusion from the defect classifier beingcreated/setup by a different user than the user evaluating the defectclassifier), being limited to deviations in classifier performance(e.g., not necessarily indicative of inspection tool performance,inspection tool recipe, tester performance, or test coverage efficacy).

Referring now to FIG. 5 , a graphical representation chart 500 of sorteddefect results 116 that are color coded to match correspondingelectrical test results 118 is illustrated. For example, defect results116 may be a quantifiable score (e.g., I-PAT score), and electrical testresults 118 may be a binary pass/fail (e.g., an overall fail if any oneof multiple electrical tests of a die fail). The chart 500 may be ahistogram chart where each thin, pixel-wide vertical line is aparticular die sorted left-to-right from worst to best, where a tallerline (higher score) is a worse, less reliable die. Further, anindividual vertical line 506 (i.e., die) with a light hash pattern asshown is a failed electrical test die 506 (Note: each pixel-widevertical line 506 is not separated from each other for clarity), and avertical line that is black in color is a passing electrical test die504. Note that the graph may be truncated and may show less than 1percent of the worst few hundred of many (e.g., thousands of) dies. Anoutlier cutoff threshold 502 may be determined such that all dies to theleft of the outlier cutoff threshold 502 are binned for discarding. Forexample, FIG. 5 may be indicative of a correlation of approximately 93percent of agreement between defect results 116 and electrical testresults 118 binned for removal. Such a correlation (or other correlationmetrics 130) may be high enough (and consistent enough) to be used inone or more embodiments of the present disclosure.

Generally speaking, as is shown by the lack (low density) of passingelectrical test dies 504 to the left side of the chart 500, it is notlikely that a die will have a high (poor) defect score and still passall electrical tests. As evidenced by the increasing density of passingelectrical test dies 504 on the right side of the chart 500 (e.g., moreblack lines), the lower the defect score is, the higher the likelihoodthat that particular die will pass an electrical test. This is, ingeneral terms, illustrative of a “correlation” as described throughoutthis disclosure. For example, (for illustrative purposes only, and notnecessarily a likely outcome) if most of the passing electrical testdies 504 were instead on the left side of the sorted chart (or randomlydispersed), rather than the right side, then such an example would beindicative of a “lack of correlation” and may indicate a malfunctioningcharacterization tool 112 or test tool 114.

Referring now to FIG. 6A, a graphical representation of a statisticalprocess control (SPC) chart 600 is illustrated, in accordance with oneor more embodiments of the present disclosure. Variations of embodimentsusing a SPC chart 600 may include adaptive sampling and/or predictivemaintenance of defect classifier 120.

In some embodiments, generating the one or more correlation metrics 130may include generating one or more process control chart data of the oneor more correlation metrics 130 configured to allow for tracking the oneor more correlation metrics 130. In some embodiments, determining adiagnosis 132 of the screening system includes monitoring a controllimit threshold corresponding to a process control chart data of the oneor more process control chart data; and identifying a control limitthreshold breach based on the control limit threshold and the processcontrol chart data.

For example, the one or more process control chart data may be aplurality of lot values 602 as shown, where each lot value 602 is avalue of a correlation metric 130 for a particular lot (of samples) 104.In some embodiments, a lower control limit threshold 606 may be used todetermine when the performance of the screening system 100 has changed.For instance, any statistical tracking method may be used in the art,such as, but not limited to, a 5 lot rolling average 604. In anotherexample, any Western Electric rule may be used to determine a breach ofa control limit 606, such as determining when 1) any single data pointfalls outside a 3σ-limit from a centerline of the data; 2) two out ofthree consecutive points fall beyond a 2σ-limit; 3) four out of fiveconsecutive points fall beyond a 1σ-limit; 4) nine consecutive pointsfall on the same side of a centerline; and/or the like.

If it is determined that a breach 608 of the control limited threshold606 has occurred, then a diagnosis 132 may be generated, triggered,updated, determined, transmitted, and the like. Alert 610 may be analert diagnosis configured to alert a user or module of a decrease inperformance.

Referring now to FIGS. 6B and 6C, an example of a particular failurecase of a die-level misalignment of the characterization tool 112 andthe test tool 114 is illustrated. FIG. 6B illustrates a graphicalrepresentation 620 of a process control chart of a die-misalignmentassociated with a screening of a wafer. FIG. 6C illustrates adiagrammatic representation 624 of the wafer of FIG. 6B.

As shown in FIG. 6C, the wafer may include many dies (e.g., dies 626,630, 632). Dies may generally be binned based on detected defects. Forexample, a diagonal pattern of defects (black dots) starting nearlocation 628 may cause a diagonal pattern of dies to be binned. As shownin the present example, at some point in the screening process of theexample wafer, the test tool 114 became misalign with the dies such thatthe test tool 114 was testing a die one position to the right and oneposition above the die that the test tool 114 was configured to betesting, skewing the results. As a result, when mapped to the wafer, thepattern of electrical screened dies 632 binned (inked off) using theelectrical test results were incorrectly binned compared to defectscreened dies 630 that were more accurately binned. Such a misalignmentevent may cause massive false-positive (overkill) and false-negative(underkill) binning.

As shown in FIG. 6B, in relation to FIG. 6C, such an event may cause asudden drop 622 in lot values 602 (of a correlation metric) below acontrol threshold 606. This sudden drop may be used to alert a user ofsuch a misalignment. In some embodiments, such a misalignment may beused in methods related to inline defect die screening; inline metrologydie screening; kill ratio, kill probability analysis; yield prediction;and/or the like.

Referring now to FIG. 7 , a graphical representation 700 of a plot line702 of test coverage and test time (usable in a test recipe of a testtool 114 (e.g., test program assessment)) is illustrated, as may be usedtypically.

Generally, higher test coverage provides greater protection againstdefect escapes. However, the test time required generally increasesexponentially as test coverage approaches 100%. Cost-benefit point 704(e.g., AEC specification for stuck-at-fault coverage) may be an optimalpoint of test coverage (such as 98%), considering the test time (e.g.,labor/cost). Test time may be adjusted over the lifetime of themanufacturing of a device. These adjustments may incorporateimprovements to the test program that address gaps which may have beenfound. However, often, an adjustment to the test program is to reducetest coverage as the manufacturing of the sample improves or in responseto “cost down” pressures from the customer. Benefits of at least someembodiments of the present disclosure may allow for higher test coverageand/or less cost than is typically achieved. For example, thecorrelation module 106 may be used to provide correlation metrics 130that allow an improved test program assessment (e.g., tradeoff betweentest time and test coverage).

In some embodiments, the one or more correlation metrics 130 may be usedto provide valuable feedback and/or to optimize/adjust an electricaltest recipe. For example, typically, Design-for-Test (DFT) personnel mayneed to optimize electrical test costs for a particular screeningprocess. Fault models may be used to identify patterns required todetect electrical faults at most points in a sample (circuit of a die)equating to high coverage. Generally, higher coverage can often beachieved by more test times and more engineering times to write thetests, but this comes at the expense of more cost/labor.

FIG. 8 is a flow diagram depicting a method 800 (or process) fordetermining a diagnosis 132 of a screening system 100 configured inaccordance with the present disclosure. For example, controller 122 maybe configured to be communicatively coupled to one or more sampleanalysis tools and may include processors configured to execute programinstructions causing the one or more processors to perform the steps ofmethod 800 (and any step, method, or the like of this disclosure).

At step 802, defect results 116 for a population of dies 104 based oninline characterization tool data received from at least one inlinecharacterization tool 112 of one or more sample analysis tools of ascreening system 100 may be identified (determined, received, acquired,generated, and the like).

At step 804, electrical test results 118 for the population of dies 104based on electrical test data received from at least one electrical testtool 144 of the one or more sample analysis tools may be identified(determined, received, acquired, generated, and the like).

At a step 806, one or more correlation metrics 130 based on theidentified defect results 116 and the identified electrical test results118 may be generated.

At a step 808, at least one diagnosis 132 of the screening system basedon the one or more correlation metrics 130 may be determined, the atleast one diagnosis 132 corresponding to a performance of the screeningsystem 100. For example, determining the at least one diagnosis 132 mayinclude acquiring a diagnostic module 108 configured to determine the atleast one diagnosis 132 of the screening system 100; and determining theat least one diagnosis 132 via the diagnostic module 108.

Referring again to FIG. 1A, embodiments of various components aredescribed in additional detail.

As noted previously herein, the controller 122 of screening system 100may include one or more processors 124 and memory 126. The memory 126may include program instructions configured to cause the one or moreprocessors 124 to carry out various steps of the present disclosure.

In another embodiment, the display of the user interface 102 may beconfigured to display data of screening system 100 to a user.

As noted previously herein, the one or more processors 124 of thecontroller 122 may be communicatively coupled to memory 126, wherein theone or more processors 124 may be configured to execute a set of programinstructions maintained in memory 126, and the set of programinstructions may be configured to cause the one or more processors 124to carry out various functions and steps of the present disclosure.

It is noted herein that the one or more components of screening system100 may be communicatively coupled to the various other components ofscreening system 100 in any manner known in the art. For example, theone or more processors 124 may be communicatively coupled to each otherand other components via a wireline (e.g., copper wire, fiber opticcable, and the like) or wireless connection (e.g., RF coupling, IRcoupling, WiMax, Bluetooth, 3G, 4G, 4G LTE, 5G, and the like). By way ofanother example, the controller 122 may be communicatively coupled toone or more components of screening system 100 via any wireline orwireless connection known in the art.

In one embodiment, the one or more processors 124 may include any one ormore processing elements known in the art. In this sense, the one ormore processors 124 may include any microprocessor-type deviceconfigured to execute software algorithms and/or instructions. In oneembodiment, the one or more processors 124 may consist of a desktopcomputer, mainframe computer system, workstation, image computer,parallel processor, or other computer system (e.g., networked computer)configured to execute a program configured to operate the screeningsystem 100, as described throughout the present disclosure. It should berecognized that the steps described throughout the present disclosuremay be carried out by a single computer system or, alternatively,multiple computer systems. Furthermore, it should be recognized that thesteps described throughout the present disclosure may be carried out onany one or more of the one or more processors 124. In general, the term“processor” may be broadly defined to encompass any device having one ormore processing elements, which execute program instructions from memory126. Moreover, different subsystems of the screening system 100 (e.g.,characterization tool 112, test tool 114, controller 122, user interface102, and the like) may include processor or logic elements suitable forcarrying out at least a portion of the steps described throughout thepresent disclosure. Therefore, the above description should not beinterpreted as a limitation on the present disclosure but merely anillustration.

The memory 126 may include any storage medium known in the art suitablefor storing program instructions executable by the associated one ormore processors 124 and the data received from the screening system 100.For example, the memory 126 may include a non-transitory memory medium.For instance, the memory 126 may include, but is not limited to, aread-only memory (ROM), a random-access memory (RAM), a magnetic oroptical memory device (e.g., disk), a magnetic tape, a solid-state driveand the like. It is further noted that memory 126 may be housed in acommon controller housing with the one or more processors 124. In analternative embodiment, the memory 126 may be located remotely withrespect to the physical location of the processors 124, controller 122,and the like. In another embodiment, the memory 126 maintains programinstructions for causing the one or more processors 124 to carry out thevarious steps described through the present disclosure.

In one embodiment, the user interface 102 is communicatively coupled tothe controller 122. The user interface 102 may include, but is notlimited to, one or more desktops, tablets, smartphones, smart watches,or the like. In another embodiment, the user interface 102 includes adisplay used to display data of the screening system 100 to a user. Thedisplay of the user interface 102 may include any display known in theart. For example, the display may include, but is not limited to, aliquid crystal display (LCD), an organic light-emitting diode (OLED)based display, or a CRT display. Those skilled in the art shouldrecognize that any display device capable of integration with a userinterface 102 is suitable for implementation in the present disclosure.In another embodiment, a user may input selections and/or instructionsresponsive to data displayed to the user via a user input device of theuser interface 102. For example, a user may view (or a controller may beconfigured to display) one or more correlation metrics 130, a diagnosis132, or an improvement. In at least one embodiment, the screening systemis configured to display a graphical user interface on the userinterface 102, where the graphical user interface includes quantitativerepresentations of correlation metrics 130 and improvements (e.g.,recommendations).

All of the methods described herein may include storing results of oneor more steps of the method embodiments in memory. The results mayinclude any of the results described herein and may be stored in anymanner known in the art. The memory may include any memory describedherein or any other suitable storage medium known in the art. After theresults have been stored, the results can be accessed in the memory andused by any of the method or system embodiments described herein,formatted for display to a user, used by another software module,method, or system, and the like. Furthermore, the results may be stored“permanently,” “semi-permanently,” temporarily,” or for some period oftime. For example, the memory may be random access memory (RAM), and theresults may not necessarily persist indefinitely in the memory.

It is further contemplated that each of the embodiments of the methoddescribed above may include any other step(s) of any other method(s)described herein. In addition, each of the embodiments of the methoddescribed above may be performed by any of the systems and/or componentsdescribed herein.

One skilled in the art will recognize that the herein describedcomponents operations, devices, objects, and the discussion accompanyingthem are used as examples for the sake of conceptual clarity and thatvarious configuration modifications are contemplated. Consequently, asused herein, the specific exemplars set forth and the accompanyingdiscussion are intended to be representative of their more generalclasses. In general, use of any specific exemplar is intended to berepresentative of its class, and the non-inclusion of specificcomponents, operations, devices, and objects should not be taken aslimiting.

As used herein, directional terms such as “top,” “bottom,” “over,”“under,” “upper,” “upward,” “lower,” “down,” and “downward” are intendedto provide relative positions for purposes of description, and are notintended to designate an absolute frame of reference. Variousmodifications to the described embodiments will be apparent to thosewith skill in the art, and the general principles defined herein may beapplied to other embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations are not expressly set forth herein for sakeof clarity.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, other components. It isto be understood that such depicted architectures are merely exemplary,and that in fact many other architectures can be implemented whichachieve the same functionality. In a conceptual sense, any arrangementof components to achieve the same functionality is effectively“associated” such that the desired functionality is achieved. Hence, anytwo components herein combined to achieve a particular functionality canbe seen as “associated with” each other such that the desiredfunctionality is achieved, irrespective of architectures or intermedialcomponents. Likewise, any two components so associated can also beviewed as being “connected,” or “coupled,” to each other to achieve thedesired functionality, and any two components capable of being soassociated can also be viewed as being “couplable,” to each other toachieve the desired functionality. Specific examples of couplableinclude but are not limited to physically mateable and/or physicallyinteracting components and/or wirelessly interactable and/or wirelesslyinteracting components and/or logically interacting and/or logicallyinteractable components.

Furthermore, it is to be understood that the invention is defined by theappended claims. It will be understood by those within the art that, ingeneral, terms used herein, and especially in the appended claims (e.g.,bodies of the appended claims) are generally intended as “open” terms(e.g., the term “including” should be interpreted as “including but notlimited to,” the term “having” should be interpreted as “having atleast,” the term “includes” should be interpreted as “includes but isnot limited to,” and the like). It will be further understood by thosewithin the art that if a specific number of an introduced claimrecitation is intended, such an intent will be explicitly recited in theclaim, and in the absence of such recitation no such intent is present.For example, as an aid to understanding, the following appended claimsmay contain usage of the introductory phrases “at least one” and “one ormore” to introduce claim recitations. However, the use of such phrasesshould not be construed to imply that the introduction of a claimrecitation by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim recitation to inventionscontaining only one such recitation, even when the same claim includesthe introductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an” (e.g., “a” and/or “an” should typically beinterpreted to mean “at least one” or “one or more”); the same holdstrue for the use of definite articles used to introduce claimrecitations. In addition, even if a specific number of an introducedclaim recitation is explicitly recited, those skilled in the art willrecognize that such recitation should typically be interpreted to meanat least the recited number (e.g., the bare recitation of “tworecitations,” without other modifiers, typically means at least tworecitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,and the like” is used, in general such a construction is intended in thesense one having skill in the art would understand the convention (e.g.,“a system having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, and the like). In those instances where a convention analogousto “at least one of A, B, or C, and the like” is used, in general such aconstruction is intended in the sense one having skill in the art wouldunderstand the convention (e.g., “a system having at least one of A, B,or C” would include but not be limited to systems that have A alone, Balone, C alone, A and B together, A and C together, B and C together,and/or A, B, and C together, and the like). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter or without sacrificing all of its material advantages.The form described is merely explanatory, and it is the intention of thefollowing claims to encompass and include such changes. Furthermore, itis to be understood that the invention is defined by the appendedclaims.

What is claimed:
 1. A screening system comprising: a controllercommunicatively coupled to one or more sample analysis tools, whereinthe one or more sample analysis tools comprise at least one inlinecharacterization tool and at least one electrical test tool, thecontroller including one or more processors configured to executeprogram instructions causing the one or more processors to: identifydefect results for a population of dies based on inline characterizationtool data received from the at least one inline characterization tool ofthe one or more sample analysis tools; identify electrical test resultsfor the population of dies based on electrical test data received fromthe at least one electrical test tool of the one or more sample analysistools; generate one or more correlation metrics based on the identifieddefect results and the identified electrical test results; and determineat least one diagnosis of the screening system based on the one or morecorrelation metrics, the at least one diagnosis corresponding to aperformance of the screening system.
 2. The screening of claim 1,wherein the defect results are identified via a defect classifier of theat least one inline characterization tool.
 3. The screening system ofclaim 2, wherein the one or more correlation metrics comprise aclassifier confidence metric corresponding to an aggregate confidencescore of the defect classifier.
 4. The screening system of claim 2,wherein the one or more processors are further configured to the executeprogram instructions causing the one or more processors to determine areduced required frequency of a manual spot-check defect classifiermaintenance of the defect classifier.
 5. The screening system of claim2, wherein the at least one diagnosis of the screening system comprisesa degradation diagnosis indicative of a defect classifier performance ofthe defect classifier of the at least one inline characterization tool.6. The screening system of claim 5, wherein the one or more processorsare further configured to execute the program instructions causing theone or more processors to: determine a defect classifier improvementbased on the degradation diagnosis, the defect classifier improvementcorresponding to at least one of: adjusting of at least one of anattribute or threshold of the defect classifier; or retraining of amachine learning model of the defect classifier.
 7. The screening systemof claim 1, wherein the at least one inline characterization toolcomprises: at least one of an inspection tool or a metrology tool. 8.The screening system of claim 1, wherein the population of diescomprises: at least one of dies in a sample, dies in multiple samples ina lot, or dies in multiple samples in multiple lots.
 9. The screeningsystem of claim 1, wherein the identified defect results and theidentified electrical test results are based on mutually exclusivesources of data such that each is an independent indication of areliability of the population of dies.
 10. The screening system of claim1, wherein the one or more correlation metrics comprise a binning ratiometric corresponding to a ratio between a number of dies of thepopulation of dies binned for removal based upon the identified defectresults and a number of dies of the population of dies binned forremoval based upon the identified electrical test results.
 11. Thescreening system of claim 1, wherein the determine the at least onediagnosis comprises: acquiring a diagnostic module configured todetermine the at least one diagnosis of the screening system; anddetermining the at least one diagnosis via the diagnostic module. 12.The screening system of claim 11, wherein the diagnostic modulecomprises a machine learning model trained for correlating multiple setsof training correlation metrics and multiple sets of one or moretraining diagnosis.
 13. The screening system of claim 1, wherein the oneor more processors are further configured to execute programinstructions causing the one or more processors to: determine animprovement of the performance of the screening system based on the atleast one diagnosis.
 14. The screening system of claim 13, wherein theimprovement of the performance of the screening system comprises atleast one of: reducing at least one of a false positive rate or a falsenegative rate of the at least one inline characterization tool; orreducing at least one of a false positive rate or a false negative rateof the at least one electrical test tool.
 15. The screening system ofclaim 14, wherein the at least one diagnosis comprises a die layoutmisalignment diagnosis indicative of a die misalignment of the at leastone electrical test tool relative to the at least one inlinecharacterization tool.
 16. The screening system of claim 14, wherein theat least one diagnosis comprises an inline defect recipe deviationdiagnosis indicative of a change in an inline defect recipe of the atleast one inline characterization tool.
 17. The screening system ofclaim 14, wherein the at least one diagnosis comprises an inlinecharacterization tool deviation diagnosis indicative of a deviation inat least one of hardware or software of the at least one inlinecharacterization tool.
 18. The screening system of claim 17, wherein thehardware comprises a degrading illumination source, wherein theimprovement of the performance of the screening system comprisesreplacing the degrading illumination source.
 19. The screening system ofclaim 1, wherein the at least one diagnosis comprises at least one of: amisalignment between the at least one electrical test tool and the atleast one inline characterization tool; a predicted maintenance intervalof a component of the screening system; a deviation of an inline defectinspection recipe; a deviation in a software and/or hardware of the atleast one inline characterization tool; or a deviation in a performanceof the at least one electrical test tool.
 20. The screening system ofclaim 1, wherein the generate the one or more correlation metricscomprises generating one or more process control chart data of the oneor more correlation metrics configured to allow for tracking the one ormore correlation metrics.
 21. The screening system of claim 20, whereinthe determining the at least one diagnosis of the screening systemcomprises: monitoring a control limit threshold corresponding to aprocess control chart data of the one or more process control chartdata; and identifying a control limit threshold breach based on thecontrol limit threshold and the process control chart data.
 22. Thescreening system of claim 1, wherein the one or more correlation metricscomprise one or more per-class correlation metrics corresponding to oneor more correlations between a class of defect results and theelectrical test results.
 23. The screening system of claim 1, whereinthe one or more correlation metrics comprise one or more per-classderivative correlation metrics corresponding to one or more derivativecorrelations between a derivative of an attribute of one or moreattributes of a class of defect results and the electrical test results.24. A method for screening comprising: identifying defect results for apopulation of dies based on inline characterization tool data receivedfrom at least one inline characterization tool of one or more sampleanalysis tools of a screening system; identifying electrical testresults for the population of dies based on electrical test datareceived from at least one electrical test tool of the one or moresample analysis tools; generating one or more correlation metrics basedon the identified defect results and the identified electrical testresults; and determining at least one diagnosis of the screening systembased on the one or more correlation metrics, the at least one diagnosiscorresponding to a performance of the screening system.
 25. The methodof claim 24, wherein the defect results are identified via a defectclassifier of the at least one inline characterization tool.
 26. Themethod of claim 25, wherein the one or more correlation metrics comprisea classifier confidence metric corresponding to an aggregate confidencescore of the defect classifier.
 27. The method of claim 25, wherein theat least one diagnosis comprises a degradation diagnosis indicative of adefect classifier performance of the defect classifier of the at leastone inline characterization tool.
 28. The method of claim 25, furthercomprising determining a reduced required frequency of a manualspot-check defect classifier maintenance of the defect classifier. 29.The method of claim 27, further comprising: determining a defectclassifier improvement based on the degradation diagnosis, the defectclassifier improvement corresponding to at least one of: adjusting of atleast one of an attribute or threshold of the defect classifier; orretraining of a machine learning model of the defect classifier.
 30. Themethod of claim 24, wherein the at least one inline characterizationtool comprises: at least one of an inspection tool or a metrology tool.31. The method of claim 24, wherein the population of dies comprises: atleast one of dies in a sample, dies in multiple samples in a lot, ordies in multiple samples in multiple lots.
 32. The method of claim 24,wherein the identified defect results and the identified electrical testresults are based on mutually exclusive sources of data such that eachis an independent indication of a reliability of the population.
 33. Themethod of claim 24, wherein the one or more correlation metrics comprisea binning ratio metric corresponding to a ratio between a number of diesof the population of dies binned for removal based upon the identifieddefect results and a number of dies of the population of dies binned forremoval based upon the identified electrical test results.
 34. Themethod of claim 24, wherein the determining the at least one diagnosiscomprises: acquiring a diagnostic module configured to determine the atleast one diagnosis of the screening system; and determining the atleast one diagnosis via the diagnostic module.
 35. The method of claim34, wherein the diagnostic module comprises a machine learning modeltrained for correlating multiple sets of training correlation metricsand multiple sets of one or more training diagnosis.
 36. The method ofclaim 24, further comprising: determining an improvement of theperformance of the screening system based on the at least one diagnosis.37. The method of claim 36, wherein the improvement of the performanceof the screening system comprises at least one of: reducing at least oneof a false positive rate or a false negative rate of the at least oneinline characterization tool; or reducing at least one of a falsepositive rate or a false negative rate of the at least one electricaltest tool.
 38. The method of claim 37, wherein the at least onediagnosis comprises a die layout misalignment diagnosis indicative of adie misalignment of the at least one electrical test tool relative tothe at least one inline characterization tool.
 39. The method of claim37, wherein the at least one diagnosis comprises an inline defect recipedeviation diagnosis indicative of a change in an inline defect recipe ofthe at least one inline characterization tool.
 40. The method of claim37, wherein the at least one diagnosis comprises an inlinecharacterization tool deviation diagnosis indicative of a deviation inat least one of hardware or software of the at least one inlinecharacterization tool.
 41. The method of claim 40, wherein the hardwarecomprises a degrading illumination source, wherein the improvement ofthe performance of the screening system comprises replacing thedegrading illumination source.
 42. The method of claim 24, wherein theat least one diagnosis comprises at least one of: a misalignment betweenthe at least one electrical test tool and the at least one inlinecharacterization tool; a predicted maintenance interval of a componentof the screening system; a deviation of an inline defect inspectionrecipe; a deviation in a software and/or hardware of the at least oneinline characterization tool; or a deviation in a performance of the atleast one electrical test tool.
 43. The method of claim 24, wherein thegenerating the one or more correlation metrics comprises generating oneor more process control chart data of the one or more correlationmetrics configured to allow for tracking the one or more correlationmetrics.
 44. The method of claim 43, wherein the determining the atleast one diagnosis of the screening system comprises: monitoring acontrol limit threshold corresponding to a process control chart data ofthe one or more process control chart data; and identifying a controllimit threshold breach based on the control limit threshold and theprocess control chart data.
 45. The method of claim 24, wherein the oneor more correlation metrics comprise one or more per-class correlationmetrics corresponding to one or more correlations between a class ofdefect results and the electrical test results.
 46. The method of claim24, wherein the one or more correlation metrics comprise one or moreper-class derivative correlation metrics corresponding to one or morederivative correlations between a derivative of an attribute of one ormore attributes of a class of defect results and the electrical testresults.